Professor Chih-Ling Tsai is a recognized expert in the practical
application of statistics in business, including regression
analysis, model selection, high-dimensional data, time series and
biostatistics. He has had more than 100 research papers published
in academic journals relating to statistics, marketing, finance
and biostatistics. He teaches courses on forecasting and
managerial research methods, and time series analysis and
forecasting.

Tsai earned a Ph.D. in statistics from the University
of Minnesota and a M.S. in mathematics from
the University of Illinois, Chicago Circle. He earned his
B.S. in mathematics from Tamkang University.

Recognizing his internationally renowned research contributions
and teaching excellence, UC Davis honored Professor
Chih-Ling Tsai with the title of Distinguished
Professor. The designation is the highest campus-level
professional faculty title.

This book by Professor Chih-Ling Tsai and co-author Allan D. R.
McQuarrie from North Dakota State University describes procedures
for selecting a model from a large set of competing statistical
models.

In partially linear single-index models, Professor Chih-Ling Tsai
and co-authors Hua Liang and Xiang Liu from the University of
Rochester and Runze Li from Pennsylvania State University obtain
the semiparametrically efficient profile least-squares estimators
of regression coefficients. The authors also employ the smoothly
clipped absolute deviation penalty (SCAD) approach to
simultaneously select variables and estimate regression
coefficients. The study shows that the resulting SCAD estimators
are consistent and possess the oracle property.

In this study, Professor Chih-Ling Tsai and co-authors Yiyun
Zhang and Runze Li apply the nonconcave penalized likelihood
approach to obtain variable selections as well as shrinkage
estimators. This approach relies heavily on the choice of
regularization parameter, which controls the model complexity.

In this study, Professors Chih-Ling Tsai and co-authors Ning Zhu
from the Shanghai Advanced Institute of Finance
and Ming-Chun Wang from National Chengchi University use a
data set from market participants in the Taiwan Stock Exchange
Capitalization Weighted Stock Index options markets to
demonstrate a strong positive relationship between prior trading
outcomes and subsequent risk taking. In particular, investors in
this market take above-average risks in afternoon trading after
morning gains.

In this paper, Professors Prasad Naik and Chih-Ling Tsai, with
co-author Peide Shi from Nuclear Safety Solutions Ltd., examine
the problem of jointly selecting the number of components and
variables in finite mixture regression models.

In Markov-switching regression models, Professors Prasad Naik,
Chih-Ling Tsai and co-author Aaron Smith from the UC Davis
Department of Agricultural and Resource Economics use
Kullback–Leibler (KL) divergence between the true and candidate
models to select the number of states and variables
simultaneously.

Inverse regression methods facilitate dimension-reduction
analyses of high-dimensional data by extracting a small number of
factors that are linear combinations of the original predictor
variables. But the estimated factors may not lend themselves
readily to interpretation consistent with prior information.

in this paper, Professors Prasad Naik and Chih-Ling Tsai derive a
new model selection criterion for single-index models, AIC, by
minimizing the expected Kullback-Leibler distances between the
true and candidate models.

The pro-posed criterion selects not only relevant variables but
also the smoothing parameter for an unknown link function. Thus,
it is a general selection criterion that provides a unifies
approach to model selection across both parametric and
nonparametric functions. Monte Carlo studies demonstrate that AIC
performs satisfactorily in most situations.

The partial least squares (PLS) approach first constructs new
explanatory variables, known as factors (or components), which
are linear combinations of available predictor variables. A small
subset of these factors is then chosen and retained for
prediction.

In data-rich marketing environments (e.g., direct marketing or
new product design), managers face an ever-growing need to reduce
the number of variables effectively. To accomplish this goal,
Professors Prasad Naik and Chih-Ling Tsai and co-author Michael
Hagerty introduce a new method called sliced inverse regression
(SIR), which finds factors by taking into account the information
contained in both the dependent and independent variables.

Commercial market research firms provide information on
advertising variables of interest, such as brand awareness or
gross rating points, that are likely to contain measurement
errors. This unreliability of measured variables induces bias in
the estimated parameters of dynamic models of advertising.
Consequently, advertisers either under- or overspend on
advertising to maintain a desired level of brand awareness.